Forecasts of Cancer and Chronic Patients: Big Data Metrics of Population Health

Forecasts of Cancer and Chronic Patients: Big Data Metrics of Population Health

Chronic diseases and cancer account for over 75 percent of healthcare costs
in the US. Increased prevention services and improved primary care are thought
to decrease costs. Current models for detecting changes in the health of
populations are cumbersome and expensive, and are not sensitive in the short
term. In this paper we model population health as a dynamical system to predict
the time evolution of the new diagnosis of chronic diseases and cancer. This
provides a reliable forecasting tool and a means of measuring short-term
changes in the health status of the population resulting from preventive care
programs. Twelve month forecasts of cancer and chronic populations were
accurate with errors lying between 3 percent and 6 percent. We confirmed what
other studies have demonstrated that diabetes patients are at increased cancer
risk but, interestingly, we also discovered that all of the studied chronic
conditions increased cancer risk just as diabetes did, and by a similar amount.
The model(i)yields a new metric for measuring performance of preventive and
clinical care programs that can provide timely feedback for quality improvement
programs;(ii)helps understand "savings" in the context of preventive care
programs and explains how they can be calculated in the short term, even though
they materialize only in the long term and(iii)provides an analytic tool and
metrics to infer correlations and derive insights on the effect of changes in
socio-economic factors affecting population health on improving health and
lowering costs of populations.